Performance metrics are generally defined to measure and monitor the health of the telecommunication network's performance. Generally, engineering/operation departments within the company focus their energy and efforts on analyzing defined performance metrics and triggering appropriate actions to maintain or improve the metrics being analyzed. This process typically originates with defining key performance indicators (KPI), which the engineers/operators use in a Root Cause Analysis (RCA) for eventually enabling actions to optimize network performance.
Currently, such RCA based on performance data and KPI are manual processes performed by experienced engineers, wherein the KPI are broken down into components and a correlation analysis is applied thereto, for identifying the root cause of a network issue. For example, data from various network elements (i.e., eNB, MME, RNC) is gathered in data warehouses and aggregated at different hierarchical levels (i.e., cell, market, region, national) and different time periods (i.e., hourly, daily). Engineers then manually query the data warehouse based on analytical needs by creating custom or pre-defined queries using the front end or a graphical user interface (GUI). The engineers typically utilize reporting tools to generate KPI reports and analyze the data, which may lead to additional analysis until the end goal of the analysis, a root cause, is reached. Such an analysis is extremely time consuming and inefficient, as it can take the engineers several days to perform the analysis.
With the enormous growth in the telecom industry and the emergence of Big Data, analytic engines have been developed to increase the efficiency of existing manual network analysis processes. These engines can process large amounts of data to assess network health, perform trouble shooting, predict network demands and manage customer needs, for example. However, limitations exist in these analytics engines. These engines still require manual steps, which as indicated above, are both time and cost inefficient. In addition, the methodology utilized in these engines is not consistent and systematic, as the skill level of the engineers/operators conducting the analysis can vary greatly. These engines also sometimes require repetition of the manual process, which again reduces the time efficiency of the analysis, because past experiences/learning are not captured for future use.